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3 Advantages of Using AI for Resume Screening

3 Advantages of Using AI for Resume Screening

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What Is Resume Screening ?

Resume screening is a process where you identify if the candidate is suitable for the role or not. It has been a manual process for decades. An ATS or recruiting software may act as resume screening software too. However, most of these softwares use keyword matching methods which are error prone. AI in recruitment has challenged the traditional approaches. Use of AI for resume screening can automate this process with much higher accuracy. Such automation can screen thousands of resumes in matter of few seconds.

AI for resume screening

According to a survey by Korn Ferry, 63% of talent professional believes that AI has changed the way recruitment is done in their organization.

Recruiters that are using AI for resume screening are clearly at an advantage over recruiters who are either doing it manually or using traditional Applicant Tracking System (ATS) for resume screening. AI recruiting software offers some big advantages over traditional methods. Let’s look at some of these advantages.

Freeing Up Recruiters Time

On an average, every job post receives about 250 candidate applications. If a recruiter spends about 2 minutes for screening a resume, it is 500 minutes a day. A Resume screening software can do that in 2 seconds. Yes! in 2 seconds.

In addition to that, using AI for resume screening can rank these candidates based on your hiring pattern. So, shortlisting of candidates happen right away.

With more time available, recruiter can spend their time talking to most promising candidates and increase their chance of finding right talent faster.

Engage Best Candidates

Recruiters process resumes in the order they receive it or find it. Many a times they either receive applications that are not relevant or when they search for candidates on job boards, they are often mislead by error-prone keyword matching techniques. e.g. when recruiters are searching for Oracle developers they end up finding many Java developers because both kind of candidates have mentioned pl-sql on their resume. Similarly it happens for Financial Analyst vs Data Analyst or Company Secretary vs Personal Secretary. The examples are countless.

Basically, recruiter may end up spending lot of time in engaging non relevant candidates. When you have already spent days in engaging not so good candidates, you may tend to believe that good candidates are difficult to find. It is then you start thinking about making compromises.

Use of AI for resume screening eliminates such noise from your resume screening process. It identifies best candidates much early in the hiring process. Therefore chances of you interviewing most suitable candidates are significantly higher.

AI For Resume Screening Can Reduce Unconscious Bias

Believe it or not but we tend to have unconscious bias towards many things. Recruitment is no exception. Many a times a hiring manager or a recruiter makes a decision by just reading the name of a candidate. Candidate demographics such as gender, race, religion or region can fuel such unconscious bias.

Use of AI for resume screening can eliminate such biases. Influential factors such as demographic details can be completely ignored while screening candidates.

Diversity is an important factor in an organization’s success. Hence it is important to address such unconscious biases that may restrict diversity at your organization.

Final Thoughts

In a very competitive talent driven market you want to reach quality candidates faster and engage them better. Social media platforms are working as fuel to attract more active and passive candidates. With volume of candidate applications is always going up, you need an approach like using AI for resume screening to quickly identify best candidates from the rest. Here is a useful reference guide on using ai for resume screening.

Amit Gawande

Amit Gawande is a Co-Founder of CVViZ, an AI recruiting software. He has more than 15 years of experience in software development and leading large teams. He has built products using NLP and machine learning. He has recruited engineers, programmers, marketing and sales people for his organizations. He believes in using technology for solving real-life problems.